Meta-Learning Update Rules for Unsupervised Representation Learning

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Abstract

A major goal of unsupervised learning is to discover data representations that are
useful for subsequent tasks, without access to supervised labels during training.
Typically, this involves minimizing a surrogate objective, such as the negative
log likelihood of a generative model, with the hope that representations useful
for subsequent tasks will arise as a side effect. In this work, we propose instead
to directly target later desired tasks by meta-learning an unsupervised learning
rule which leads to representations useful for those tasks. Specifically, we target
semi-supervised classification performance, and we meta-learn an algorithm –
an unsupervised weight update rule – that produces representations useful for
this task. Additionally, we constrain our unsupervised update rule to a be a
biologically-motivated, neuron-local function, which enables it to generalize to
different neural network architectures, datasets, and data modalities. We show that
the meta-learned update rule produces useful features and sometimes outperforms
existing unsupervised learning techniques. We further show that the meta-learned
unsupervised update rule generalizes to train networks with different widths, depths,
and nonlinearities. It also generalizes to train on data with randomly permuted
input dimensions and even generalizes from image datasets to a text task.